Hakkari Üniversitesi Akademik Veri Yönetim Sistemi
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Evaluation of the Wellness of Children’s and Affecting Factors during the COVID-19 Pandemic Process
No full textObjective: This study was carried out to assess the well-being of children and adolescents under lockdown conditions during the COVID-19 pandemic and the factors affecting it. Material and Methods: This descriptive and cross-sectional study was conducted with 282 parents of children aged between 3-14. “Socio-Demographic Data Collection Form” and “The Well-Being of Children in Lockdown Scale (WCLS)” were used in data collection. Descriptive statistics and multiple regression analysis were used to analyze the data. Results: The majority of participants’ (97.9%, n=276) total scores on the Well-being of Children in Lockdown Scale ranged between 45 and 66, and the level of their well-being was moderate. It was found that eleven variables explained 8.7% of the variance in the total score of the Well-being of Children in Lockdown Scale (R2=0.087, p=0.009). The variables that had a significant effect on the scores of the sub-dimensions of the scale were the age of the mother (p=0.006), the financial status of the family (p=0.004) and the number of children (p=0.010) in the physical activity sub-dimension; the status of going to school (p<0.001), financial status of family (p=0.001) and the child’s age (p=0.001) in the addiction sub-dimension; the age of the mother (p=0.004), the age of the father (p<0.001) and father’s employment status (p=0.003) in the emotions sub-dimension; the child’s age (p=0.048), the age of the father (p=0.046) and father’s employment status (p=0.010) in the fun and creative activities sub-dimension. Conclusion: In this study, the well-being level of children and adolescents was determined to be moderate. It is recommended to plan studies on other variables that can predict children and adolescents’ well-being and to make timely interventions necessary for them
VIBRATION CONTROL OF LANDING GEAR IN VERTICAL TAKE-OFF AND LANDING AIRCRAFT USING ARTFICIAL NEURAL NETWORK BASED APPROACHES
No full textVertical Takeoff and Landing(VTOL) aircraft have gained strategic importance in both military and civiliansectors in recent years due to their ability to operate without the need for arunway. Their flexibility, especially in limited spaces such as urbantransportation, search and rescue operations, marine platforms, and aircraftcarriers, makes these vehicles indispensable for the future of air transport.However, one of the most significant challenges for VTOL aircraft is thehigh-amplitude vibrations that occur in the landing gear during the verticallanding and takeoff phases. These vibrations accelerate structural fatigue dueto impact loads transferred to the fuselage, shorten the lifespan of landinggear components, and seriously reduce passenger comfort. During landing, groundroughness, aerodynamic effects caused by rotor or jet currents, the groundeffect phenomenon, and sudden load changes significantly complicate thedynamics of the landing gear.In this study, an artificialneural network (ANN)-based control approach has been developed to suppressvibrations occurring in the landing gear of VTOL aircraft. The landing gear wasmodeled as a mass-spring-damper system, and random inputs from the ground wereincluded in the model. The ANN-based controller aims to minimize verticalaccelerations transmitted to the body, keep stroke usage within safe limits,and reduce impact loads generated during landing. A comprehensive performanceanalysis was conducted by comparing the proposed method with classical PIDcontrol and semi-active damping strategies.The simulation resultsobtained show that the ANN-based approach exhibits superior performance,particularly in reducing peak acceleration, impact forces, and structuralloads. This result contributes to VTOL aircraft achieving higher standards inboth safety and comfort. In conclusion, artificial neural network-based controlmethods offer a powerful and viable solution for landing gear vibration controlin future eVTOL concepts and urban air mobility applications.Keywords: Vertical Take-Off and Landing (VTOL),Landing Gear Dynamics, Artificial Neural Networks, Vibration Control</p
CORRUPTION, RULE OF LAW, AND ACCOUNTABILITY THEIR ROLES INGOVERNMENT EFFICIENCY IN TURKIYE
No full textThe Determining Role of Socio-Cultural Factors in the Branch Choice of Male Volleyball Players
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Application of Monte Carlo simulation and stochastic fractional search algorithm for solar PV placement considering diverse solar radiations
No full textThis study employs Monte Carlo Simulation (MCS) within the structure of the Stochastic Fractional Search Algorithm (SFSA) to address circumstances involving uncertainty. The goal is to improve the system's performance by creating probability distribution functions for bus voltages and branch currents. We will use the resultant distribution in chance-constrained stochastic scheduling. The objective of the present research is to analyze the impact of uncertainties in the operation of photovoltaic (PV) systems, specifically in relation to different solar radiation conditions, on the amount of power loss. The approach focuses on including stochastic constraints in distribution systems instead of depending solely on precise deterministic boundaries. The goal is to enhance efficiency and ensure optimal consumption of power. This research enhances the knowledge base on PV unit positioning in distribution systems by integrating meta-heuristic optimization and MCS into a comprehensive framework. The investigation centers on the implementation of a chance-constrained method. We evaluate the optimization results using MCS under various uncertainty scenarios to demonstrate the effectiveness of the recommended approach. Furthermore, we conduct an analysis to assess the likelihood of exceeding the system's boundaries. The strategy's effectiveness is assessed by comparing the results of the SFSA with the Firefly algorithm (FA) utilizing probabilistic evaluation and simulation
Analysis on Solar Power Plant Placement and Distribution Grid Issues Utilizing Monte Carlo Simulation and Teaching Learning-Based Optimization
No full textThis study investigates the impact that uncertainties in photovoltaic (PV) performance under a variety of solar irradiance scenarios have on the expected power losses that occur when probabilistic distribution network restrictions are taken into consideration. Through the use of Teaching-Learning-Based Optimization (TLBO) and Monte Carlo Simulation (MCS) in conjunction with an emphasis on chance-constrained approaches, this study contributes to the enhancement of the body of literature for optimal PV installation in distribution networks. The outputs of the optimization are validated using MCS under a variety of uncertainty scenarios, and the variables of the distribution network are evaluated in terms of the probability of exceeding constraints. This is done in order to demonstrate that the suggested technique is effective. A comparison is made between the outcomes of TLBO and the implementation of Artificial Bee Colony (ABC) technique, which make use of probabilistic evaluation and modeling. It has been shown via simulation that the TLBO methodology is superior than the ABC method in terms of efficiently minimizing power losses
